Baxter and Rennie say it better than me, so I will summarize them.
Suppose that $N_t$ is not stochastic and $f(.)$ is a smooth function then the Taylor expansion is
$$
df(N_t) = f'(N_t)dN_t + \frac{1}{2}f''(N_t)(dN_t)^2 + \frac{1}{3!} f'''(N_t)(dN_t)^3 + \ldots
$$
and the term $(dN_T)^2$ and higher terms are zero. Ito showed that this is not the case in the ...

In fact Ito and Stratonovich calculus are both mathematically equivalent. In the following paper you can e.g. see that both derivations lead to the same result, i.e. the Black-Scholes equation:
Black-Scholes option pricing within Ito and Stratonovich conventions by J. Perello, J. M. Porra, M. Montero and J. Masoliver
From the abstract:
Options financial ...

I will provide some references such that you can see where the different processes are used. These papers typically motivate their models and show which effect the single paramaters have and what asset price dynamics the model intends to capture.
Geometric Brownian motion
The geometric Brownian motion is the easiest model for exponential growth with ...

$X_t$ being a stochastic process, one cannot use ordinary calculus to express the differential of a (sufficiently well-behaved) function $f$ of $t$ and $X_t$.
Instead one should turn to Itô's lemma, one of the key results of stochastic calculus, which stipulates (assuming $X_t$ is here a continuous, square integrable stochastic process)
$$ df(t,X_t) = \frac{...

If by 'solve' you mean how do we know that $\ln S_t$ is the right change of variable, then you can go by the following (not rigorous) line of thought:
Ito's fomula suggests that given an SDE $$dX_t = \mu(X_t,t)dt+\sigma(X_t,t)dW_t$$ and a function $f(x,t)$: the SDE for the process $Y_t=f(X_t,t)$ will satisfy $$dY_t = [f_t(X_t,t) + f_x(X_t,t)\mu(X_t,t) + \...

First, for Ito processes and Brownian motion. Ito process is a continuous-time trajectory with random evolution, so non-smooth and very kinky - also has a fractal look: no matter how much you'd zoom in, it will look similar. Ito process consists in fact of two parts: the drift part (deterministic evolution) and the diffusion part (where all the kinkiness and ...

It is indeed Riemann integrable, so you don't need stochastic integration. For a given path, you can interpret the integral in the Riemann sense. For a given t, the paths are random, so it is a random variable.
You can also express it as an Ito’s process. To see the connection, just apply ito's lemma to $tW_t$:
$d \left(tW_t\right)=tdW_t+W_tdt$
$W_tdt=d \...

As usual with those kind of integrals, another way to reach the result is to:
Express $W_s$ in integral form as $\int_0^s dW_u$
Use Fubini theorem to change the integration bounds of the resulting double integral
More specifically,
\begin{align}
\int_0^t W_s ds &= \int_0^t \int_0^s dW_u ds \\
&= \int_0^t \int_u^t ds dW_u \\
...

You are "deriving" with respect to $t$ (the time index in your stochastic process).
$f(t,x) = x^2$ so $f(t,W_t) = W_t^2$. And Ito's lemma tells you
$W_b^2 - W_a^2 = \int_{t=a}^b d(W_t^2) = \int_{t=a}^b df(t,W_t) = \int_{t=a}^b 2W_t dW_t + \int_{t=a}^b dt$ for all $0 \le a <= b$.
PS: Actually you are not deriving. The differential notation is just a ...

In stochastic calculus, only stochastic integrals are defined. The differential form is just a notation. That is, $$dF=g(t)dW_t$$ is just another expression for the integral $$F=\int_0^t g(s) dW_s.$$ See, for example, in this book or this book, all Ito's lemmas are expressed in integral forms.
For your question, note that $F$ is not a function of $t$ and $...

I thought this was an interesting example to add. It concerns a "ratio model" of habit (as opposed to a "difference" model of habit). See, for example, Abel (1990, American Economic Review). Let
$$
x_t = \lambda \int_{-\infty}^t e^{-\lambda(t-s)} c_s ds.
$$
(For context, $x_t$ is a log habit index that is given by a geometric average of past consumption, ...

What can be shown is that the above expressions are equal in probability.
First check the distribution. As any linear combination of a Gaussian is Gaussian the right hand side is Gaussian - the left hand side too. Then we need the 2 moments:
The expected values - it is zero ... easy to see.
Next what you did not specify is that the correlation between $...

You seem to use the term "volatility" to describe two very different quantities: (1) the diffusion coefficient of your SDE and (2) the standard deviation of the log-returns under your modelling assumptions. While the first may be negative, the second may not.
[Interpretation 1]
Consider a probability space $(\Omega,\mathcal{F},\mathbb{P})$ and a standard ...

May I point your attention to my following paper, where I address this question in an intuitive manner on page 12:
von Jouanne-Diedrich, Holger, Ito, Stratonovich and Friends (May 18, 2017).Available at SSRN: https://ssrn.com/abstract=2956257
The basic idea in the discrete case is to go through all possible branches and then square the differences. In each ...

The second solution is correct, so the solution at t is:
$F_t^2=F_0^2e^{2 \sigma W_t-\sigma^2t}$
Now apply expectation to both sides.
$ E\left[F_t^2\right]=F_0^2 \, E\left[e^{2 \sigma W_t-\sigma^2t}\right]$
The term in the exponent is just Gaussian so let’s call it X:
$X=2 \sigma W_t-\sigma^2t$
It’s mean and variance are:
$E[X]=-\sigma^2t$
$V[X]=4 \...